{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6a92a3c-9ce6-44c2-9cb8-6af744881ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets,transforms\n",
    "import time\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199e244a-b415-4c84-ab56-a7fca1f00350",
   "metadata": {},
   "source": [
    "# AlexNet的PyTorch复现（猫狗大战）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946778a2-b441-4efb-8009-e1a4104c0c03",
   "metadata": {},
   "source": [
    "## 1. 数据集制作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb549c1-46e8-4e52-8545-323b455cb09c",
   "metadata": {},
   "source": [
    "在论文中AlexNet作者使用的是ILSVRC 2012比赛数据集，该数据集非常大（有138G），下载、训练都很消耗时间，我们在复现的时候就不用这个数据集了。由于MNIST、CIFAR10、CIFAR100这些数据集图片尺寸都较小，不符合AlexNet网络输入尺寸227x227的要求，因此我们改用kaggle比赛经典的“猫狗大战”数据集了。\n",
    "\n",
    "该数据集包含的训练集总共25000张图片，猫狗各12500张，带标签；测试集总共12500张，不带标签。我们仅使用带标签的25000张图片，分别拿出2500张猫和狗的图片作为模型的验证集。我们按照以下目录层级结构，将数据集图片放好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f8534d-c0c8-4c4e-800b-fb9250040303",
   "metadata": {},
   "source": [
    "![](./images/path.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0f385c-7f9a-46d2-baa9-54df705f4063",
   "metadata": {},
   "source": [
    "为了方便大家训练，我们将该数据集放在百度云盘，下载链接：\n",
    "链接：https://pan.baidu.com/s/1UEOzxWWMLCUoLTxdWUkB4A \n",
    "提取码：cdue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e99da6-9283-4517-9bbe-db8c3e21a8b3",
   "metadata": {},
   "source": [
    "### 1.1 制作图片数据的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5361e7a5-d0fc-441d-878b-37ae5defc27a",
   "metadata": {},
   "source": [
    "准备好数据集之后，我们需要用PyTorch来读取并制作可以用来训练和测试的数据集。对于训练集和测试集，首先要分别制作对应的图片数据索引，即train.txt和test.txt两个文件，每个txt中包含每个图片的目录和对应类别class（cat对应的label=0，dog对应的label=1）。示意图如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ccf4725-6ef0-4a60-92a5-c1e9af136f36",
   "metadata": {},
   "source": [
    "![](./images/index.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466a0b40-46e1-49c1-bf9b-a5f4cc1dfa8b",
   "metadata": {},
   "source": [
    "制作图片数据索引train.txt和test.txt两个文件的python脚本程序如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac69b05c-f400-48a2-89f8-67bd8eb51a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "train_txt_path = os.path.join(\"data\", \"catVSdog\", \"train.txt\")\n",
    "train_dir = os.path.join(\"data\", \"catVSdog\", \"train_data\")\n",
    "valid_txt_path = os.path.join(\"data\", \"catVSdog\", \"test.txt\")\n",
    "valid_dir = os.path.join(\"data\", \"catVSdog\", \"test_data\")\n",
    "\n",
    "def gen_txt(txt_path, img_dir):\n",
    "    f = open(txt_path, 'w')\n",
    "    \n",
    "    for root, s_dirs, _ in os.walk(img_dir, topdown=True):  # 获取 train文件下各文件夹名称\n",
    "        for sub_dir in s_dirs:\n",
    "            i_dir = os.path.join(root, sub_dir)             # 获取各类的文件夹 绝对路径\n",
    "            img_list = os.listdir(i_dir)                    # 获取类别文件夹下所有png图片的路径\n",
    "            for i in range(len(img_list)):\n",
    "                if not img_list[i].endswith('jpg'):         # 若不是png文件，跳过\n",
    "                    continue\n",
    "                #label = (img_list[i].split('.')[0] == 'cat')? 0 : 1 \n",
    "                label = img_list[i].split('.')[0]\n",
    "                # 将字符类别转为整型类型表示\n",
    "                if label == 'cat':\n",
    "                    label = '0'\n",
    "                else:\n",
    "                    label = '1'\n",
    "                img_path = os.path.join(i_dir, img_list[i])\n",
    "                line = img_path + ' ' + label + '\\n'\n",
    "                f.write(line)\n",
    "    f.close()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    gen_txt(train_txt_path, train_dir)\n",
    "    gen_txt(valid_txt_path, valid_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e57734-f5ba-44c9-98db-9d14ab383cdd",
   "metadata": {},
   "source": [
    "运行脚本之后就在./data/catVSdog/目录下生成train.txt和test.txt两个索引文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e8754b-92b3-40c0-909a-1af3c416db07",
   "metadata": {},
   "source": [
    "### 1.2 构建Dataset子类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef1d69e-2151-4c58-92e5-2a7e4f7d2805",
   "metadata": {},
   "source": [
    "PyTorch 加载自己的数据集，需要写一个继承自torch.utils.data中Dataset类，并修改其中的__init__方法、__getitem__方法、__len__方法。默认加载的都是图片，__init__的目的是得到一个包含数据和标签的list，每个元素能找到图片位置和其对应标签。然后用__getitem__方法得到每个元素的图像像素矩阵和标签，返回img和label。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce26676d-705e-4dda-93b5-f4dbeda7c4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, txt_path, transform = None, target_transform = None):\n",
    "        fh = open(txt_path, 'r')\n",
    "        imgs = []\n",
    "        for line in fh:\n",
    "            line = line.rstrip()\n",
    "            words = line.split()\n",
    "            imgs.append((words[0], int(words[1]))) # 类别转为整型int\n",
    "            self.imgs = imgs \n",
    "            self.transform = transform\n",
    "            self.target_transform = target_transform\n",
    "    def __getitem__(self, index):\n",
    "        fn, label = self.imgs[index]\n",
    "        img = Image.open(fn).convert('RGB') \n",
    "        #img = Image.open(fn)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img) \n",
    "        return img, label\n",
    "    def __len__(self):\n",
    "        return len(self.imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c2cf3a-71af-47ad-814d-c833d0fc72a3",
   "metadata": {},
   "source": [
    "getitem是核心函数。self.imgs是一个list，self.imgs[index]是一个str，包含图片路径，图片标签，这些信息是从上面生成的txt文件中读取；利用Image.open对图片进行读取，注意这里的img是单通道还是三通道的；self.transform(img)对图片进行处理，这个transform里边可以实现减均值、除标准差、随机裁剪、旋转、翻转、放射变换等操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654ae94f-b3ff-4033-81ea-1c5a9aad8328",
   "metadata": {},
   "source": [
    "### 1.3 加载数据集和数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d0a907-646d-4493-9eb0-fa80075d2ec2",
   "metadata": {},
   "source": [
    "当Mydataset构建好，剩下的操作就交给DataLoder来加载数据集。在DataLoder中，会触发Mydataset中的getiterm函数读取一张图片的数据和标签，并拼接成一个batch返回，作为模型真正的输入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48cad5df-cd98-4fb9-91d3-4016d40c8fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipline_train = transforms.Compose([\n",
    "    #transforms.RandomResizedCrop(224),\n",
    "    #随机旋转图片\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    #将图片尺寸resize到227x227\n",
    "    transforms.Resize((227,227)),\n",
    "    #将图片转化为Tensor格式\n",
    "    transforms.ToTensor(),\n",
    "    #正则化(当模型出现过拟合的情况时，用来降低模型的复杂度)\n",
    "    #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "])\n",
    "pipline_test = transforms.Compose([\n",
    "    #将图片尺寸resize到227x227\n",
    "    transforms.Resize((227,227)),\n",
    "    transforms.ToTensor(),\n",
    "    #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "])\n",
    "train_data = MyDataset('./data/catVSdog/train.txt', transform=pipline_train)\n",
    "test_data = MyDataset('./data/catVSdog/test.txt', transform=pipline_test)\n",
    "\n",
    "#train_data 和test_data包含多有的训练与测试数据，调用DataLoader批量加载\n",
    "trainloader = torch.utils.data.DataLoader(dataset=train_data, batch_size=64, shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(dataset=test_data, batch_size=32, shuffle=False)\n",
    "# 类别信息也是需要我们给定的\n",
    "classes = ('cat', 'dog') # 对应label=0，label=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3ac33b-ff3e-48a4-80c9-897216d2fd7c",
   "metadata": {},
   "source": [
    "在数据预处理中，我们将图片尺寸调整到227x227，符合AlexNet网络的输入要求。这是使用的是ImageNet的均值和标准差。使用Imagenet的均值和标准差是一种常见的做法。它们是根据数百万张图像计算得出的。如果要在自己的数据集上从头开始训练，则可以计算新的均值和标准差。均值mean = [0.485, 0.456, 0.406]，方差std = [0.229, 0.224, 0.225]，然后使用transforms.Normalize进行归一化操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae3fda9-8530-48da-917b-4a8d7c8d1c97",
   "metadata": {},
   "source": [
    "我们来看一下最终制作的数据集图片和它们对应的标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4db2c891-cde9-42f3-88fd-af09766c498d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "examples = enumerate(trainloader)\n",
    "batch_idx, (example_data, example_label) = next(examples)\n",
    "# 批量展示图片\n",
    "for i in range(4):\n",
    "    plt.subplot(1, 4, i + 1)\n",
    "    plt.tight_layout()  #自动调整子图参数，使之填充整个图像区域\n",
    "    img = example_data[i]\n",
    "    img = img.numpy() # FloatTensor转为ndarray\n",
    "    img = np.transpose(img, (1,2,0)) # 把channel那一维放到最后\n",
    "    #img = img * [0.5, 0.5, 0.5] + [0.5, 0.5, 0.5]\n",
    "    img = img * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]\n",
    "    plt.imshow(img)\n",
    "    plt.title(\"label:{}\".format(example_label[i]))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f389978a-256e-47b6-a99e-e8bce85d5a8f",
   "metadata": {},
   "source": [
    "## 2. 搭建AlexNet神经网络结构，并定义前向传播的过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "717de392-91f1-45cb-99b5-d3f967cc628f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AlexNet(nn.Module):\n",
    "    \"\"\"\n",
    "    Neural network model consisting of layers propsed by AlexNet paper.\n",
    "    \"\"\"\n",
    "    def __init__(self, num_classes=2):\n",
    "        \"\"\"\n",
    "        Define and allocate layers for this neural net.\n",
    "        Args:\n",
    "            num_classes (int): number of classes to predict with this model\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # input size should be : (b x 3 x 227 x 227)\n",
    "        # The image in the original paper states that width and height are 224 pixels, but\n",
    "        # the dimensions after first convolution layer do not lead to 55 x 55.\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4),  # (b x 96 x 55 x 55)\n",
    "            nn.ReLU(),\n",
    "            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),  # section 3.3\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 96 x 27 x 27)\n",
    "            nn.Conv2d(96, 256, 5, padding=2),  # (b x 256 x 27 x 27)\n",
    "            nn.ReLU(),\n",
    "            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 256 x 13 x 13)\n",
    "            nn.Conv2d(256, 384, 3, padding=1),  # (b x 384 x 13 x 13)\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 384, 3, padding=1),  # (b x 384 x 13 x 13)\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 256, 3, padding=1),  # (b x 256 x 13 x 13)\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 256 x 6 x 6)\n",
    "        )\n",
    "        # classifier is just a name for linear layers\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(p=0.5, inplace=True),\n",
    "            nn.Linear(in_features=(256 * 6 * 6), out_features=500),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5, inplace=True),\n",
    "            nn.Linear(in_features=500, out_features=20),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=20, out_features=num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Pass the input through the net.\n",
    "        Args:\n",
    "            x (Tensor): input tensor\n",
    "        Returns:\n",
    "            output (Tensor): output tensor\n",
    "        \"\"\"\n",
    "        x = self.net(x)\n",
    "        x = x.view(-1, 256 * 6 * 6)  # reduce the dimensions for linear layer input\n",
    "        return self.classifier(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3dc23f-be47-479e-a4c9-d26021a57646",
   "metadata": {},
   "source": [
    "在构建AlexNet网络里，参数num_classes指的是类别的数量，由于论文中AlexNet的输出是1000个类别，我们这里的数据集只有猫和狗两个类别，因此这里的全连接层的神经元个数做了微调。num_classes=2，输出层也是两个神经元，不是原来的1000个神经元。FC6由原来的4096个神经元改为500个神经元，FC7由原来的4096个神经元改为20个神经元。这里的改动大家注意一下，根据实际数据集的类别数量进行调整。整个网络的其它结构跟论文中的完全一样。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72294575-f2f3-478d-ba62-e5f947bed569",
   "metadata": {},
   "source": [
    "## 3. 将定义好的网络结构搭载到GPU/CPU，并定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1a3414b-f53d-466d-b57c-7774993451b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建模型，部署gpu\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = AlexNet().to(device)\n",
    "#定义优化器\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93db0c3c-2961-4d66-8a8a-57578839b15c",
   "metadata": {},
   "source": [
    "## 4. 定义训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88ad4b6e-60ee-4059-a0e1-c2dff765895d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_runner(model, device, trainloader, optimizer, epoch):\n",
    "    #训练模型, 启用 BatchNormalization 和 Dropout, 将BatchNormalization和Dropout置为True\n",
    "    model.train()\n",
    "    total = 0\n",
    "    correct =0.0\n",
    "    \n",
    "    #enumerate迭代已加载的数据集,同时获取数据和数据下标\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "        #把模型部署到device上\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        #初始化梯度\n",
    "        optimizer.zero_grad()\n",
    "        #保存训练结果\n",
    "        outputs = model(inputs)\n",
    "        #计算损失和\n",
    "        #多分类情况通常使用cross_entropy(交叉熵损失函数), 而对于二分类问题, 通常使用sigmod\n",
    "        loss = F.cross_entropy(outputs, labels)\n",
    "        #获取最大概率的预测结果\n",
    "        #dim=1表示返回每一行的最大值对应的列下标\n",
    "        predict = outputs.argmax(dim=1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predict == labels).sum().item()\n",
    "        #反向传播\n",
    "        loss.backward()\n",
    "        #更新参数\n",
    "        optimizer.step()\n",
    "        if i % 100 == 0:\n",
    "            #loss.item()表示当前loss的数值\n",
    "            print(\"Train Epoch{} \\t Loss: {:.6f}, accuracy: {:.6f}%\".format(epoch, loss.item(), 100*(correct/total)))\n",
    "            Loss.append(loss.item())\n",
    "            Accuracy.append(correct/total)\n",
    "    return loss.item(), correct/total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc23dc69-01b9-464f-8f13-abcc2b722cd2",
   "metadata": {},
   "source": [
    "## 5. 定义测试过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fca551bd-6f5c-40d0-9a3e-db6f704677d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_runner(model, device, testloader):\n",
    "    #模型验证, 必须要写, 否则只要有输入数据, 即使不训练, 它也会改变权值\n",
    "    #因为调用eval()将不启用 BatchNormalization 和 Dropout, BatchNormalization和Dropout置为False\n",
    "    model.eval()\n",
    "    #统计模型正确率, 设置初始值\n",
    "    correct = 0.0\n",
    "    test_loss = 0.0\n",
    "    total = 0\n",
    "    #torch.no_grad将不会计算梯度, 也不会进行反向传播\n",
    "    with torch.no_grad():\n",
    "        for data, label in testloader:\n",
    "            data, label = data.to(device), label.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.cross_entropy(output, label).item()\n",
    "            predict = output.argmax(dim=1)\n",
    "            #计算正确数量\n",
    "            total += label.size(0)\n",
    "            correct += (predict == label).sum().item()\n",
    "        #计算损失值\n",
    "        print(\"test_avarage_loss: {:.6f}, accuracy: {:.6f}%\".format(test_loss/total, 100*(correct/total)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b14c4d-f2ba-43dd-9789-9a5b4a2b61bd",
   "metadata": {},
   "source": [
    "## 6. 运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e84fa14c-1af2-4654-b268-1aadae280cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_time 2022-02-08 08:43:47\n",
      "Train Epoch1 \t Loss: 0.706899, accuracy: 46.875000%\n",
      "Train Epoch1 \t Loss: 0.691226, accuracy: 51.871906%\n",
      "Train Epoch1 \t Loss: 0.633129, accuracy: 53.482587%\n",
      "Train Epoch1 \t Loss: 0.627048, accuracy: 55.351952%\n",
      "test_avarage_loss: 0.020481, accuracy: 61.800000%\n",
      "end_time:  2022-02-08 09:12:29 \n",
      "\n",
      "start_time 2022-02-08 09:12:29\n",
      "Train Epoch2 \t Loss: 0.661039, accuracy: 65.625000%\n",
      "Train Epoch2 \t Loss: 0.671636, accuracy: 63.010520%\n",
      "Train Epoch2 \t Loss: 0.677994, accuracy: 63.401741%\n",
      "Train Epoch2 \t Loss: 0.604137, accuracy: 63.797757%\n",
      "test_avarage_loss: 0.020667, accuracy: 65.960000%\n",
      "end_time:  2022-02-08 09:41:27 \n",
      "\n",
      "start_time 2022-02-08 09:41:27\n",
      "Train Epoch3 \t Loss: 0.760604, accuracy: 57.812500%\n",
      "Train Epoch3 \t Loss: 0.630579, accuracy: 66.073639%\n",
      "Train Epoch3 \t Loss: 0.562116, accuracy: 66.775498%\n",
      "Train Epoch3 \t Loss: 0.604609, accuracy: 66.990241%\n",
      "test_avarage_loss: 0.017850, accuracy: 71.220000%\n",
      "end_time:  2022-02-08 10:10:15 \n",
      "\n",
      "start_time 2022-02-08 10:10:15\n",
      "Train Epoch4 \t Loss: 0.549495, accuracy: 73.437500%\n",
      "Train Epoch4 \t Loss: 0.777462, accuracy: 70.374381%\n",
      "Train Epoch4 \t Loss: 0.528823, accuracy: 70.304726%\n",
      "Train Epoch4 \t Loss: 0.562505, accuracy: 71.028862%\n",
      "test_avarage_loss: 0.015732, accuracy: 75.860000%\n",
      "end_time:  2022-02-08 10:38:46 \n",
      "\n",
      "start_time 2022-02-08 10:38:46\n",
      "Train Epoch5 \t Loss: 0.538889, accuracy: 75.000000%\n",
      "Train Epoch5 \t Loss: 0.564642, accuracy: 73.329208%\n",
      "Train Epoch5 \t Loss: 0.577222, accuracy: 73.647388%\n",
      "Train Epoch5 \t Loss: 0.424103, accuracy: 74.262874%\n",
      "test_avarage_loss: 0.015013, accuracy: 76.960000%\n",
      "end_time:  2022-02-08 11:07:02 \n",
      "\n",
      "start_time 2022-02-08 11:07:02\n",
      "Train Epoch6 \t Loss: 0.476291, accuracy: 73.437500%\n",
      "Train Epoch6 \t Loss: 0.513430, accuracy: 76.810025%\n",
      "Train Epoch6 \t Loss: 0.407145, accuracy: 76.803483%\n",
      "Train Epoch6 \t Loss: 0.321917, accuracy: 77.310008%\n",
      "test_avarage_loss: 0.014570, accuracy: 78.540000%\n",
      "end_time:  2022-02-08 11:35:19 \n",
      "\n",
      "start_time 2022-02-08 11:35:19\n",
      "Train Epoch7 \t Loss: 0.249209, accuracy: 92.187500%\n",
      "Train Epoch7 \t Loss: 0.416248, accuracy: 79.548267%\n",
      "Train Epoch7 \t Loss: 0.380385, accuracy: 79.889614%\n",
      "Train Epoch7 \t Loss: 0.475330, accuracy: 79.993771%\n",
      "test_avarage_loss: 0.012805, accuracy: 80.680000%\n",
      "end_time:  2022-02-08 12:03:28 \n",
      "\n",
      "start_time 2022-02-08 12:03:28\n",
      "Train Epoch8 \t Loss: 0.371355, accuracy: 82.812500%\n",
      "Train Epoch8 \t Loss: 0.292748, accuracy: 80.925124%\n",
      "Train Epoch8 \t Loss: 0.409115, accuracy: 81.133396%\n",
      "Train Epoch8 \t Loss: 0.573240, accuracy: 81.431686%\n",
      "test_avarage_loss: 0.011869, accuracy: 82.940000%\n",
      "end_time:  2022-02-08 12:31:24 \n",
      "\n",
      "start_time 2022-02-08 12:31:24\n",
      "Train Epoch9 \t Loss: 0.405602, accuracy: 85.937500%\n",
      "Train Epoch9 \t Loss: 0.352403, accuracy: 82.100866%\n",
      "Train Epoch9 \t Loss: 0.264721, accuracy: 82.276119%\n",
      "Train Epoch9 \t Loss: 0.418451, accuracy: 82.257060%\n",
      "test_avarage_loss: 0.011898, accuracy: 83.040000%\n",
      "end_time:  2022-02-08 12:59:22 \n",
      "\n",
      "start_time 2022-02-08 12:59:22\n",
      "Train Epoch10 \t Loss: 0.446182, accuracy: 76.562500%\n",
      "Train Epoch10 \t Loss: 0.347864, accuracy: 83.230198%\n",
      "Train Epoch10 \t Loss: 0.427435, accuracy: 83.053483%\n",
      "Train Epoch10 \t Loss: 0.271487, accuracy: 83.191445%\n",
      "test_avarage_loss: 0.011292, accuracy: 83.820000%\n",
      "end_time:  2022-02-08 13:27:15 \n",
      "\n",
      "start_time 2022-02-08 13:27:15\n",
      "Train Epoch11 \t Loss: 0.302349, accuracy: 84.375000%\n",
      "Train Epoch11 \t Loss: 0.299067, accuracy: 84.173886%\n",
      "Train Epoch11 \t Loss: 0.294397, accuracy: 83.745336%\n",
      "Train Epoch11 \t Loss: 0.396348, accuracy: 83.700166%\n",
      "test_avarage_loss: 0.011781, accuracy: 82.980000%\n",
      "end_time:  2022-02-08 13:55:38 \n",
      "\n",
      "start_time 2022-02-08 13:55:38\n",
      "Train Epoch12 \t Loss: 0.289707, accuracy: 89.062500%\n",
      "Train Epoch12 \t Loss: 0.342575, accuracy: 84.282178%\n",
      "Train Epoch12 \t Loss: 0.258070, accuracy: 84.678172%\n",
      "Train Epoch12 \t Loss: 0.253973, accuracy: 84.831811%\n",
      "test_avarage_loss: 0.011146, accuracy: 83.960000%\n",
      "end_time:  2022-02-08 14:24:32 \n",
      "\n",
      "start_time 2022-02-08 14:24:32\n",
      "Train Epoch13 \t Loss: 0.277147, accuracy: 87.500000%\n",
      "Train Epoch13 \t Loss: 0.274984, accuracy: 84.514233%\n",
      "Train Epoch13 \t Loss: 0.296342, accuracy: 84.872512%\n",
      "Train Epoch13 \t Loss: 0.346535, accuracy: 85.112126%\n",
      "test_avarage_loss: 0.011816, accuracy: 83.960000%\n",
      "end_time:  2022-02-08 14:53:23 \n",
      "\n",
      "start_time 2022-02-08 14:53:23\n",
      "Train Epoch14 \t Loss: 0.184067, accuracy: 95.312500%\n",
      "Train Epoch14 \t Loss: 0.384374, accuracy: 86.556312%\n",
      "Train Epoch14 \t Loss: 0.266996, accuracy: 86.007463%\n",
      "Train Epoch14 \t Loss: 0.301216, accuracy: 85.906354%\n",
      "test_avarage_loss: 0.010429, accuracy: 85.800000%\n",
      "end_time:  2022-02-08 15:21:39 \n",
      "\n",
      "start_time 2022-02-08 15:21:39\n",
      "Train Epoch15 \t Loss: 0.351465, accuracy: 81.250000%\n",
      "Train Epoch15 \t Loss: 0.233513, accuracy: 86.602723%\n",
      "Train Epoch15 \t Loss: 0.305769, accuracy: 86.256219%\n",
      "Train Epoch15 \t Loss: 0.356608, accuracy: 86.269726%\n",
      "test_avarage_loss: 0.010304, accuracy: 85.820000%\n",
      "end_time:  2022-02-08 15:50:07 \n",
      "\n",
      "start_time 2022-02-08 15:50:07\n",
      "Train Epoch16 \t Loss: 0.233803, accuracy: 89.062500%\n",
      "Train Epoch16 \t Loss: 0.228391, accuracy: 86.448020%\n",
      "Train Epoch16 \t Loss: 0.366558, accuracy: 86.466107%\n",
      "Train Epoch16 \t Loss: 0.239762, accuracy: 86.420266%\n",
      "test_avarage_loss: 0.011253, accuracy: 83.960000%\n",
      "end_time:  2022-02-08 16:18:48 \n",
      "\n",
      "start_time 2022-02-08 16:18:48\n",
      "Train Epoch17 \t Loss: 0.341136, accuracy: 84.375000%\n",
      "Train Epoch17 \t Loss: 0.191537, accuracy: 86.819307%\n",
      "Train Epoch17 \t Loss: 0.487172, accuracy: 86.761505%\n",
      "Train Epoch17 \t Loss: 0.226308, accuracy: 86.949751%\n",
      "test_avarage_loss: 0.010429, accuracy: 85.900000%\n",
      "end_time:  2022-02-08 16:47:39 \n",
      "\n",
      "start_time 2022-02-08 16:47:39\n",
      "Train Epoch18 \t Loss: 0.285305, accuracy: 84.375000%\n",
      "Train Epoch18 \t Loss: 0.230307, accuracy: 87.855817%\n",
      "Train Epoch18 \t Loss: 0.313802, accuracy: 87.624378%\n",
      "Train Epoch18 \t Loss: 0.286835, accuracy: 87.629776%\n",
      "test_avarage_loss: 0.010459, accuracy: 85.360000%\n",
      "end_time:  2022-02-08 17:16:02 \n",
      "\n",
      "start_time 2022-02-08 17:16:02\n",
      "Train Epoch19 \t Loss: 0.386397, accuracy: 84.375000%\n",
      "Train Epoch19 \t Loss: 0.283305, accuracy: 88.706683%\n",
      "Train Epoch19 \t Loss: 0.281673, accuracy: 87.935323%\n",
      "Train Epoch19 \t Loss: 0.267362, accuracy: 87.884136%\n",
      "test_avarage_loss: 0.010591, accuracy: 85.380000%\n",
      "end_time:  2022-02-08 17:43:45 \n",
      "\n",
      "start_time 2022-02-08 17:43:45\n",
      "Train Epoch20 \t Loss: 0.285401, accuracy: 87.500000%\n",
      "Train Epoch20 \t Loss: 0.193409, accuracy: 87.747525%\n",
      "Train Epoch20 \t Loss: 0.193623, accuracy: 87.624378%\n",
      "Train Epoch20 \t Loss: 0.221007, accuracy: 87.816653%\n",
      "test_avarage_loss: 0.010428, accuracy: 86.000000%\n",
      "end_time:  2022-02-08 18:11:27 \n",
      "\n",
      "Finished Training\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#调用\n",
    "epoch = 20\n",
    "Loss = []\n",
    "Accuracy = []\n",
    "for epoch in range(1, epoch+1):\n",
    "    print(\"start_time\",time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))\n",
    "    loss, acc = train_runner(model, device, trainloader, optimizer, epoch)\n",
    "    Loss.append(loss)\n",
    "    Accuracy.append(acc)\n",
    "    test_runner(model, device, testloader)\n",
    "    print(\"end_time: \",time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),'\\n')\n",
    "\n",
    "print('Finished Training')\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(Loss)\n",
    "plt.title('Loss')\n",
    "plt.show()\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(Accuracy)\n",
    "plt.title('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56d7502-3fbb-4c33-a6d8-affecc1a7236",
   "metadata": {},
   "source": [
    "## 7. 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e3b3700-83b9-4979-b492-c0c7649cb284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (net): Sequential(\n",
      "    (0): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4))\n",
      "    (1): ReLU()\n",
      "    (2): LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=2)\n",
      "    (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (5): ReLU()\n",
      "    (6): LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=2)\n",
      "    (7): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (8): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU()\n",
      "    (10): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU()\n",
      "    (12): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU()\n",
      "    (14): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=True)\n",
      "    (1): Linear(in_features=9216, out_features=500, bias=True)\n",
      "    (2): ReLU()\n",
      "    (3): Dropout(p=0.5, inplace=True)\n",
      "    (4): Linear(in_features=500, out_features=20, bias=True)\n",
      "    (5): ReLU()\n",
      "    (6): Linear(in_features=20, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\.conda\\envs\\pytorch\\lib\\site-packages\\torch\\serialization.py:360: UserWarning: Couldn't retrieve source code for container of type AlexNet. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "print(model)\n",
    "torch.save(model, './models/alexnet-catvsdog.pth') #保存模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be2bb3b-3788-4f54-abfe-96a5c24f5b4a",
   "metadata": {},
   "source": [
    "## 8. 模型测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebc48f6-d6be-44c7-aa10-ed81320723d3",
   "metadata": {},
   "source": [
    "下面使用一张猫狗大战测试集的图片进行模型的测试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d555044-c066-423c-bb07-c34da473c123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "概率： tensor([[0.9983, 0.0017]], grad_fn=<SoftmaxBackward>)\n",
      "预测类别： cat\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    model = torch.load('./models/alexnet-catvsdog.pth') #加载模型\n",
    "    model = model.to(device)\n",
    "    model.eval()    #把模型转为test模式\n",
    "    \n",
    "    #读取要预测的图片\n",
    "    # 读取要预测的图片\n",
    "    img = Image.open(\"./images/test_cat.jpg\") # 读取图像\n",
    "    #img.show()\n",
    "    plt.imshow(img) # 显示图片\n",
    "    plt.axis('off') # 不显示坐标轴\n",
    "    plt.show()\n",
    "    \n",
    "    # 导入图片，图片扩展后为[1，1，32，32]\n",
    "    trans = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize((227,227)),\n",
    "            transforms.ToTensor(),\n",
    "            #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "            transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "        ])\n",
    "    img = trans(img)\n",
    "    img = img.to(device)\n",
    "    img = img.unsqueeze(0)  #图片扩展多一维,因为输入到保存的模型中是4维的[batch_size,通道,长，宽]，而普通图片只有三维，[通道,长，宽]\n",
    "    \n",
    "    # 预测 \n",
    "    # 预测 \n",
    "    classes = ('cat', 'dog')\n",
    "    output = model(img)\n",
    "    prob = F.softmax(output,dim=1) #prob是2个分类的概率\n",
    "    print(\"概率：\",prob)\n",
    "    value, predicted = torch.max(output.data, 1)\n",
    "    predict = output.argmax(dim=1)\n",
    "    pred_class = classes[predicted.item()]\n",
    "    print(\"预测类别：\",pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ea6994-08b6-4f67-9271-4ced7cacac5e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-pytorch] *",
   "language": "python",
   "name": "conda-env-.conda-pytorch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
